HomeworkWeek6

Week 6 Homework

Loading in data

library(data.table)
setwd("~/HomeworkWeek6/")
mygenes <- read.table(file = 'gene_dist_head.tsv',header=TRUE, sep = "\t", quote = "\"'")

Ashley E Noriega,

Oct 12, 2019

A script for testing out R

{ library(data.table) setwd(“~/HomeworkWeek6/”) mygenes <- read.table(file = ‘gene_dist_head.tsv’,header=TRUE, sep = “, quote =”"‘") library(’ggplot2’) library(‘plotly’) }

Summarizing “mygenes”

summary(mygenes)

##                 transcript_type           feature             chr         
##  protein_coding         :2001054   exon       :1306656   1      : 238010  
##  nonsense_mediated_decay: 293471   CDS        : 791856   2      : 189916  
##  processed_transcript   : 173401   UTR        : 304070   17     : 166529  
##  retained_intron        : 150034   transcript : 215170   19     : 163304  
##  lincRNA                :  55928   stop_codon :  73411   3      : 159475  
##  antisense              :  45811   start_codon:  73358   11     : 157597  
##  (Other)                : 108613   (Other)    :  63791   (Other):1753481  
##      start                end           
##  Min.   :      577   Min.   :      647  
##  1st Qu.: 31698816   1st Qu.: 31700419  
##  Median : 56565463   Median : 56566763  
##  Mean   : 73148763   Mean   : 73152067  
##  3rd Qu.:108204790   3rd Qu.:108206944  
##  Max.   :249230780   Max.   :249231242  
## 

Test_graph.r script

Create a vector to subset autosomes and graph new “genes” dataframe

library(ggplot2) # We load in libraries
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

# define a factor set of autosomes
autosomes<-c("1","2","3","4","5","6","7","8","9","10",
 "11","12","13","14","15","16","17","18","19","20","21","22")  
# create a new dataframe called 'genes' which only has autosomes, however we still have those additional factors
genes<- mygenes[ which(mygenes$chr %in% autosomes), ] 
# remove the factors and then order them so they are numerical order and not alphabetical
genes$chr <- factor(genes$chr, levels = autosomes) 
 # plot in ggplot 
ggplot(data = genes) +  geom_bar(mapping = aes(x = chr, fill = feature), width = 1) 

# go to polar coordinates
ggplot(data = genes) +  geom_bar(mapping = aes(x = chr, fill = feature), width = 1) + coord_polar()

Make non-polar “gene” graph interactive

library(ggplot2)
library(plotly)
p<-ggplot(data = genes) +  geom_bar(mapping = aes(x = chr, fill = feature), width = 1)
ggplotly(p)